Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method comprising: receiving, by an autonomous vehicle system, a request for use of an autonomous vehicle from a user, the request including in indication of a general goal location, the general goal location having a first degree of precision and a first degree of accuracy regarding a location of the user; instructing, by the autonomous vehicle system, the autonomous vehicle to proceed from an initial location towards the general goal location; while the autonomous vehicle is proceeding from the initial location towards the general goal location, determining, by the autonomous vehicle system, a precise goal location, the precise goal location being within a vicinity of a general goal location, the precise location having a second degree of precision and a second degree of accuracy regarding the location of the user, wherein at least one of: the second degree of precision is greater than the first degree of precision, or the second degree of accuracy is greater than the first degree of accuracy, and wherein the precise goal location is determined based on a wireless communication between the autonomous vehicle and the user when the autonomous vehicle and the user are within line-of-sight; determining, by the autonomous vehicle system, a stopping place at which the autonomous vehicle and a user will engage in a destination goal location activity, the stopping place being within a vicinity of the precise goal location; and instructing, by the autonomous vehicle system, the autonomous vehicle to proceed towards and stop at the stopping place.
Autonomous vehicle systems often struggle with precise navigation to a user's desired destination, especially when the initial request lacks detailed location information. This invention addresses this challenge by improving the accuracy and precision of autonomous vehicle routing. The system receives a user's request for an autonomous vehicle, including a general goal location with a first level of precision and accuracy. The vehicle begins moving toward this general location. As the vehicle approaches, it refines the destination by determining a more precise goal location within the vicinity of the initial target. This refinement is based on wireless communication between the vehicle and the user, which occurs when they are in line-of-sight, ensuring higher precision and accuracy than the original request. The system then identifies a specific stopping place near the precise goal location where the vehicle and user will engage in a destination-related activity, such as picking up or dropping off the user. The vehicle is then instructed to navigate to and stop at this precise stopping place. This method enhances the efficiency and reliability of autonomous vehicle navigation by dynamically refining the destination based on real-time communication and proximity.
2. The method of claim 1 , wherein the precise goal location is the actual precise location of the user.
A system and method for determining a precise goal location for navigation or tracking purposes. The invention addresses the challenge of accurately identifying a user's exact position, which is critical for applications such as emergency response, asset tracking, or autonomous navigation. Traditional methods often rely on approximate coordinates or require manual input, leading to inefficiencies or inaccuracies. The method involves determining a precise goal location by analyzing sensor data, such as GPS, inertial measurement units (IMUs), or other positioning technologies, to refine the user's position. The system may also incorporate environmental data, such as maps or obstacle detection, to further enhance accuracy. In one embodiment, the precise goal location is dynamically updated in real-time as the user moves, ensuring continuous precision. Additionally, the method may include error correction techniques to account for signal interference, multipath effects, or other sources of positional inaccuracy. Machine learning or statistical models may be applied to predict and adjust the user's position based on historical or contextual data. The system can also integrate with external databases or networks to cross-reference and validate the determined location. In a specific implementation, the precise goal location is set to the user's actual precise location, ensuring that navigation or tracking systems reference the most accurate and up-to-date position. This eliminates reliance on pre-defined or estimated coordinates, improving reliability in critical applications. The method may be deployed in wearable devices, vehicles, or fixed installations, depending on the use case.
3. The method of claim 1 , wherein the precise goal location is determined based on a distance from the autonomous vehicle to one of the precise goal location and the general goal location.
Autonomous vehicles require precise navigation to reach specific destinations, but existing systems often rely on general goal locations that lack the accuracy needed for tasks like docking or parking. This invention improves autonomous vehicle navigation by determining a precise goal location based on the vehicle's distance to either the precise or general goal location. The system first identifies a general goal location, such as a parking spot or charging station, and then refines it to a precise goal location by calculating the distance from the vehicle to either the precise or general location. This ensures the vehicle can navigate with high accuracy, avoiding errors that arise from imprecise targeting. The method dynamically adjusts the goal location based on real-time distance measurements, improving reliability in complex environments. By integrating precise distance-based adjustments, the system enhances autonomous vehicle performance in tasks requiring exact positioning, such as docking or tight-space maneuvering. The invention addresses the challenge of balancing computational efficiency with navigation precision, ensuring the vehicle reaches the intended destination accurately without excessive processing overhead.
4. The method of claim 3 , wherein the distance is determined comprising determining the distance repeatedly.
This invention relates to a method for determining a distance, particularly in applications where repeated distance measurements are necessary. The method involves repeatedly calculating the distance between two points or objects to improve accuracy, reliability, or real-time tracking. This approach is useful in various fields such as robotics, autonomous navigation, industrial automation, and surveying, where precise and continuous distance measurements are critical. The method may involve using sensors, such as LiDAR, radar, or ultrasonic devices, to capture distance data over time. By repeatedly measuring the distance, the system can account for environmental changes, movement, or noise, ensuring more accurate and consistent results. The repeated measurements may be processed using algorithms that filter out errors, average values, or detect trends, enhancing the overall precision of the distance determination. This technique is particularly valuable in dynamic environments where objects or conditions change frequently. For example, in autonomous vehicles, repeatedly measuring the distance to obstacles allows for real-time adjustments to navigation paths. Similarly, in industrial settings, continuous distance monitoring can improve safety and efficiency by detecting deviations in machinery or assembly line operations. The method may also include additional steps such as calibrating sensors, compensating for environmental factors, or integrating multiple measurement techniques to further refine the distance calculations. By employing repeated distance determinations, the system ensures robust and reliable performance in various applications.
5. The method of claim 1 , wherein the precise goal location is determined based on road data.
This invention relates to navigation systems that determine precise goal locations for route guidance. The problem addressed is the need for accurate and reliable destination identification in navigation systems, particularly when relying on road data to pinpoint specific locations. The method involves determining a precise goal location for route guidance by analyzing road data. Road data includes information such as road networks, intersections, landmarks, and other geographic features. The system processes this data to identify the most accurate and relevant location for the user's destination. This ensures that the navigation system provides precise directions, reducing errors and improving user experience. The method may also involve integrating additional data sources, such as user input, historical data, or real-time traffic information, to refine the goal location further. By leveraging road data, the system can dynamically adjust the destination coordinates to match real-world conditions, enhancing the accuracy of route calculations. This approach is particularly useful in urban environments where road networks are complex, and precise location identification is critical for efficient navigation. The system ensures that the goal location is correctly mapped to the road network, avoiding misrouting and improving overall navigation performance.
6. The method of claim 1 , wherein the precise goal location changes over time.
A system and method for dynamic navigation adjusts a precise goal location in real-time to optimize pathfinding for autonomous vehicles or robotic systems. The technology addresses the challenge of static goal locations in traditional navigation systems, which fail to account for changing environmental conditions, obstacles, or evolving mission requirements. By continuously updating the target destination based on real-time data, the system ensures efficient and adaptive routing. The method involves monitoring environmental factors such as traffic, weather, or obstacle movement, then recalculating the optimal path to a dynamically adjusted goal. This approach improves navigation efficiency, reduces travel time, and enhances safety by avoiding unpredictable obstacles or hazards. The system may integrate sensor data, machine learning algorithms, or predictive models to anticipate and respond to changes in the goal location. Applications include autonomous vehicles, drones, and industrial robots operating in dynamic environments where fixed destinations are impractical. The method ensures continuous adaptation to new conditions, improving overall system performance and reliability.
7. The method of claim 1 , further comprising: transmitting, by the autonomous vehicle system to a mobile device associated with the user, a command to generate an image or series of images using a display of the mobile device, and wherein determining the precise goal location comprises: obtaining, by the autonomous vehicle system, sensor data comprising visual information regarding an environment of the autonomous vehicle, identifying, by the autonomous vehicle system based on the sensor data, the image or the series of images in the environment of the autonomous vehicle, and determining the precise goal location based on the identification of the image or the series of images in the environment of the autonomous vehicle.
An autonomous vehicle system navigates to a precise goal location by leveraging visual cues from a user's mobile device. The system transmits a command to the mobile device to display an image or series of images on its screen. The user then presents this display to the vehicle's environment, such as holding it up or placing it in a visible location. The autonomous vehicle system captures sensor data, including visual information from cameras or other sensors, to detect the displayed image or series of images in its surroundings. By identifying these visual markers, the system determines the exact goal location based on their position relative to the vehicle. This method enhances navigation accuracy by using dynamic, user-generated visual references instead of relying solely on predefined maps or coordinates. The approach is particularly useful in scenarios where traditional navigation methods may fail, such as in complex or unstructured environments. The system processes the sensor data to match the displayed images with their real-world counterparts, ensuring precise localization and navigation to the intended destination. This technique combines real-time visual recognition with autonomous vehicle capabilities to improve navigation reliability and user interaction.
8. The method of claim 1 , further comprising: transmitting, by the autonomous vehicle system to a mobile device associated with the user, a command to generate a sound using a speaker of the mobile device, and wherein determining the precise goal location comprises: obtaining, by the autonomous vehicle system, sensor data comprising auditory information regarding an environment of the autonomous vehicle, identifying, by the autonomous vehicle system based on the sensor data, the sound in the environment of the autonomous vehicle, and determining the precise goal location based on the identification of the sound in the environment of the autonomous vehicle.
Autonomous vehicles often struggle to accurately determine a user's intended destination, especially in complex environments where visual cues alone may be insufficient. This invention addresses this challenge by leveraging auditory signals to enhance location precision. The system transmits a command to a user's mobile device, instructing it to emit a sound via its speaker. The autonomous vehicle then captures sensor data, including auditory information from the environment, to detect the sound generated by the mobile device. By analyzing this auditory data, the system identifies the sound's source and uses it to determine the precise goal location. This method improves navigation accuracy by combining auditory feedback with traditional sensor inputs, ensuring the vehicle can reliably pinpoint the user's intended destination even in ambiguous or cluttered environments. The approach enhances user experience by reducing reliance on visual markers and leveraging mobile device capabilities to refine location tracking.
9. The method of claim 1 , wherein determining the precise goal location comprises: obtaining, by the autonomous vehicle system, sensor data comprising visual information regarding an environment of the autonomous vehicle, identifying, by the autonomous vehicle system based on the sensor data, the user performing a pre-determined gesture in in the environment of the autonomous vehicle, and determining the precise goal location based on the identification of the user performing the pre-determined gesture in the environment of the autonomous vehicle.
Autonomous vehicles rely on precise goal location determination to navigate effectively, but traditional methods often require explicit user input or predefined waypoints. This invention addresses the challenge by enabling an autonomous vehicle to dynamically determine a precise goal location based on a user's gestures. The system uses onboard sensors, such as cameras, to capture visual information about the vehicle's surroundings. By analyzing this sensor data, the system identifies when a user performs a predefined gesture, such as pointing or waving, within the vehicle's environment. The system then interprets the gesture to determine the exact location the user intends the vehicle to reach. This approach eliminates the need for manual input or preprogrammed destinations, enhancing user convenience and interaction with autonomous vehicles. The gesture recognition process involves real-time analysis of visual data to detect and interpret human movements accurately, ensuring the vehicle responds to the user's intent without additional commands. This method improves the adaptability and responsiveness of autonomous vehicles in dynamic environments.
10. The method of claim 1 , further comprising: transmitting, by the autonomous vehicle system to a mobile device associated with the user, a command to generate a modulated sequence of light using the mobile device, and wherein determining the precise goal location comprises: obtaining, by the autonomous vehicle system, sensor data comprising visual information regarding an environment of the autonomous vehicle, identifying, by the autonomous vehicle system based on the sensor data, the modulated sequence of light in the environment of the autonomous vehicle, and determining the precise goal location based on the identification of the modulated sequence of light in the environment of the autonomous vehicle.
An autonomous vehicle system enhances navigation by using a user's mobile device to generate a modulated light sequence that helps the vehicle determine a precise goal location. The system transmits a command to the mobile device, instructing it to produce a specific light pattern. The autonomous vehicle then captures sensor data, including visual information of its surroundings, to detect the modulated light sequence emitted by the mobile device. By analyzing this light pattern, the vehicle identifies the exact location of the user or destination, improving navigation accuracy. This method leverages visual cues from the mobile device to refine the vehicle's understanding of the environment, ensuring precise positioning for tasks like pickup, drop-off, or navigation to a specific point. The approach combines real-time sensor data with user-generated visual signals to enhance autonomous vehicle operations in dynamic environments.
11. The method of claim 1 , wherein determining the precise goal location comprises: determining, based on the wireless communication, a bearing of a user relative to the autonomous vehicle; responsive to determining the bearing of a user relative to the autonomous vehicle, instructing the autonomous vehicle to direct a directional sensor towards the determined bearing; and determining a distance between the user and the autonomous vehicle based on sensor data obtained from the directional sensor.
This invention relates to autonomous vehicle navigation systems that improve the precision of user pickup or delivery locations. The problem addressed is the difficulty of accurately determining a user's exact location when an autonomous vehicle approaches, particularly in crowded or visually obstructed environments where GPS or other positioning systems may lack sufficient precision. The method involves using wireless communication between the user and the autonomous vehicle to establish a relative bearing. Once the bearing is determined, the autonomous vehicle adjusts a directional sensor (such as a camera, lidar, or radar) to align with that bearing. The sensor then captures data to measure the distance between the user and the vehicle. This combination of bearing and distance information allows the vehicle to pinpoint the user's precise location, enabling accurate alignment for pickup or delivery. The system enhances navigation by reducing reliance on imprecise GPS coordinates and instead leverages real-time sensor data to confirm the user's exact position. This is particularly useful in urban settings, parking lots, or other areas where traditional positioning methods may fail. The approach ensures that the autonomous vehicle can approach the user with high accuracy, improving efficiency and user experience.
Unknown
August 11, 2020
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